Overview

Dataset statistics

Number of variables26
Number of observations10687
Missing cells31363
Missing cells (%)11.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 MiB
Average record size in memory208.0 B

Variable types

DateTime1
Categorical1
Text3
Numeric19
Unsupported2

Alerts

Durée moyenne du trajet is highly overall correlated with Retard moyen des trains en retard à l'arrivée and 1 other fieldsHigh correlation
Nombre de circulations prévues is highly overall correlated with Nombre de trains en retard au départ and 4 other fieldsHigh correlation
Nombre de trains en retard au départ is highly overall correlated with Nombre de circulations prévues and 4 other fieldsHigh correlation
Nombre de trains en retard à l'arrivée is highly overall correlated with Nombre de circulations prévues and 4 other fieldsHigh correlation
Nombre trains en retard > 15min is highly overall correlated with Nombre de circulations prévues and 6 other fieldsHigh correlation
Nombre trains en retard > 30min is highly overall correlated with Nombre de circulations prévues and 6 other fieldsHigh correlation
Nombre trains en retard > 60min is highly overall correlated with Nombre de circulations prévues and 6 other fieldsHigh correlation
Retard moyen de tous les trains au départ is highly overall correlated with Nombre de trains en retard au départ and 4 other fieldsHigh correlation
Retard moyen de tous les trains à l'arrivée is highly overall correlated with Nombre trains en retard > 15min and 4 other fieldsHigh correlation
Retard moyen des trains en retard à l'arrivée is highly overall correlated with Durée moyenne du trajet and 3 other fieldsHigh correlation
Retard moyen trains en retard > 15 (si liaison concurrencée par vol) is highly overall correlated with Retard moyen des trains en retard à l'arrivéeHigh correlation
Service is highly overall correlated with Durée moyenne du trajetHigh correlation
Commentaire annulations has 10687 (100.0%) missing valuesMissing
Commentaire retards au départ has 10687 (100.0%) missing valuesMissing
Commentaire retards à l'arrivée has 9989 (93.5%) missing valuesMissing
Retard moyen de tous les trains à l'arrivée is highly skewed (γ1 = -32.31119815)Skewed
Commentaire annulations is an unsupported type, check if it needs cleaning or further analysisUnsupported
Commentaire retards au départ is an unsupported type, check if it needs cleaning or further analysisUnsupported
Nombre de trains annulés has 3591 (33.6%) zerosZeros
Nombre de trains en retard au départ has 125 (1.2%) zerosZeros
Retard moyen des trains en retard au départ has 123 (1.2%) zerosZeros
Retard moyen de tous les trains au départ has 111 (1.0%) zerosZeros
Nombre de trains en retard à l'arrivée has 188 (1.8%) zerosZeros
Retard moyen des trains en retard à l'arrivée has 222 (2.1%) zerosZeros
Retard moyen de tous les trains à l'arrivée has 121 (1.1%) zerosZeros
Nombre trains en retard > 15min has 219 (2.0%) zerosZeros
Retard moyen trains en retard > 15 (si liaison concurrencée par vol) has 203 (1.9%) zerosZeros
Nombre trains en retard > 30min has 465 (4.4%) zerosZeros
Nombre trains en retard > 60min has 1706 (16.0%) zerosZeros
Prct retard pour causes externes has 1039 (9.7%) zerosZeros
Prct retard pour cause infrastructure has 961 (9.0%) zerosZeros
Prct retard pour cause gestion trafic has 1278 (12.0%) zerosZeros
Prct retard pour cause matériel roulant has 1172 (11.0%) zerosZeros
Prct retard pour cause gestion en gare et réutilisation de matériel has 3168 (29.6%) zerosZeros
Prct retard pour cause prise en compte voyageurs (affluence, gestions PSH, correspondances) has 3614 (33.8%) zerosZeros

Reproduction

Analysis started2025-12-01 21:13:40.403586
Analysis finished2025-12-01 21:13:50.685087
Duration10.28 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Date
Date

Distinct87
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size83.6 KiB
Minimum2018-01-01 00:00:00
Maximum2025-03-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-01T22:13:50.703977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:50.735125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Service
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size83.6 KiB
National
9389 
International
1298 

Length

Max length13
Median length8
Mean length8.6072799
Min length8

Characters and Unicode

Total characters91986
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNational
2nd rowInternational
3rd rowNational
4th rowNational
5th rowNational

Common Values

ValueCountFrequency (%)
National9389
87.9%
International1298
 
12.1%

Length

2025-12-01T22:13:50.760095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-01T22:13:50.773379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
national9389
87.9%
international1298
 
12.1%

Most occurring characters

ValueCountFrequency (%)
a21374
23.2%
n13283
14.4%
t11985
13.0%
i10687
11.6%
o10687
11.6%
l10687
11.6%
N9389
10.2%
I1298
 
1.4%
e1298
 
1.4%
r1298
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)91986
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a21374
23.2%
n13283
14.4%
t11985
13.0%
i10687
11.6%
o10687
11.6%
l10687
11.6%
N9389
10.2%
I1298
 
1.4%
e1298
 
1.4%
r1298
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)91986
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a21374
23.2%
n13283
14.4%
t11985
13.0%
i10687
11.6%
o10687
11.6%
l10687
11.6%
N9389
10.2%
I1298
 
1.4%
e1298
 
1.4%
r1298
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)91986
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a21374
23.2%
n13283
14.4%
t11985
13.0%
i10687
11.6%
o10687
11.6%
l10687
11.6%
N9389
10.2%
I1298
 
1.4%
e1298
 
1.4%
r1298
 
1.4%
Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size83.6 KiB
2025-12-01T22:13:50.826128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length25
Mean length11.862169
Min length4

Characters and Unicode

Total characters126771
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGRENOBLE
2nd rowPARIS LYON
3rd rowMARSEILLE ST CHARLES
4th rowPARIS NORD
5th rowANNECY
ValueCountFrequency (%)
paris4468
21.6%
lyon2692
 
13.0%
montparnasse1392
 
6.7%
st837
 
4.1%
part522
 
2.5%
dieu522
 
2.5%
est522
 
2.5%
marseille479
 
2.3%
charles479
 
2.3%
ville348
 
1.7%
Other values (77)8396
40.6%
2025-12-01T22:13:50.908071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A13923
11.0%
S12116
9.6%
R11230
8.9%
E11059
8.7%
N10765
8.5%
9970
 
7.9%
L8649
 
6.8%
I8336
 
6.6%
O7335
 
5.8%
P7165
 
5.7%
Other values (18)26223
20.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)126771
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A13923
11.0%
S12116
9.6%
R11230
8.9%
E11059
8.7%
N10765
8.5%
9970
 
7.9%
L8649
 
6.8%
I8336
 
6.6%
O7335
 
5.8%
P7165
 
5.7%
Other values (18)26223
20.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)126771
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A13923
11.0%
S12116
9.6%
R11230
8.9%
E11059
8.7%
N10765
8.5%
9970
 
7.9%
L8649
 
6.8%
I8336
 
6.6%
O7335
 
5.8%
P7165
 
5.7%
Other values (18)26223
20.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)126771
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A13923
11.0%
S12116
9.6%
R11230
8.9%
E11059
8.7%
N10765
8.5%
9970
 
7.9%
L8649
 
6.8%
I8336
 
6.6%
O7335
 
5.8%
P7165
 
5.7%
Other values (18)26223
20.7%
Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size83.6 KiB
2025-12-01T22:13:50.965279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length30
Median length25
Mean length11.787031
Min length4

Characters and Unicode

Total characters125968
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARIS LYON
2nd rowITALIE
3rd rowLYON PART DIEU
4th rowDUNKERQUE
5th rowPARIS LYON
ValueCountFrequency (%)
paris4468
21.8%
lyon2692
 
13.1%
montparnasse1392
 
6.8%
st764
 
3.7%
part522
 
2.5%
dieu522
 
2.5%
est522
 
2.5%
marseille406
 
2.0%
charles406
 
2.0%
tgv348
 
1.7%
Other values (77)8469
41.3%
2025-12-01T22:13:51.047830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A13777
10.9%
S11897
9.4%
R11157
8.9%
E10840
8.6%
N10838
8.6%
9824
 
7.8%
L8430
 
6.7%
I8336
 
6.6%
O7481
 
5.9%
P7165
 
5.7%
Other values (18)26223
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)125968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A13777
10.9%
S11897
9.4%
R11157
8.9%
E10840
8.6%
N10838
8.6%
9824
 
7.8%
L8430
 
6.7%
I8336
 
6.6%
O7481
 
5.9%
P7165
 
5.7%
Other values (18)26223
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)125968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A13777
10.9%
S11897
9.4%
R11157
8.9%
E10840
8.6%
N10838
8.6%
9824
 
7.8%
L8430
 
6.7%
I8336
 
6.6%
O7481
 
5.9%
P7165
 
5.7%
Other values (18)26223
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)125968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A13777
10.9%
S11897
9.4%
R11157
8.9%
E10840
8.6%
N10838
8.6%
9824
 
7.8%
L8430
 
6.7%
I8336
 
6.6%
O7481
 
5.9%
P7165
 
5.7%
Other values (18)26223
20.8%

Durée moyenne du trajet
Real number (ℝ)

High correlation 

Distinct409
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.40152
Minimum0
Maximum786
Zeros73
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2025-12-01T22:13:51.296909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q1100
median163
Q3222
95-th percentile335
Maximum786
Range786
Interquartile range (IQR)122

Descriptive statistics

Standard deviation87.802397
Coefficient of variation (CV)0.5152677
Kurtosis1.1806578
Mean170.40152
Median Absolute Deviation (MAD)62
Skewness0.96833922
Sum1821081
Variance7709.2609
MonotonicityNot monotonic
2025-12-01T22:13:51.327291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96166
 
1.6%
46135
 
1.3%
97129
 
1.2%
95125
 
1.2%
98122
 
1.1%
131121
 
1.1%
132115
 
1.1%
99107
 
1.0%
13094
 
0.9%
13391
 
0.9%
Other values (399)9482
88.7%
ValueCountFrequency (%)
073
0.7%
351
 
< 0.1%
4525
 
0.2%
46135
1.3%
477
 
0.1%
4840
 
0.4%
4932
 
0.3%
5051
 
0.5%
5136
 
0.3%
523
 
< 0.1%
ValueCountFrequency (%)
7861
 
< 0.1%
5641
 
< 0.1%
5581
 
< 0.1%
5511
 
< 0.1%
5445
< 0.1%
5351
 
< 0.1%
5321
 
< 0.1%
5211
 
< 0.1%
5073
 
< 0.1%
50210
0.1%

Nombre de circulations prévues
Real number (ℝ)

High correlation 

Distinct867
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269.28829
Minimum0
Maximum1100
Zeros73
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2025-12-01T22:13:51.356164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31
Q1149
median229
Q3357
95-th percentile609
Maximum1100
Range1100
Interquartile range (IQR)208

Descriptive statistics

Standard deviation181.47806
Coefficient of variation (CV)0.67391738
Kurtosis1.8309707
Mean269.28829
Median Absolute Deviation (MAD)91
Skewness1.2099244
Sum2877884
Variance32934.287
MonotonicityNot monotonic
2025-12-01T22:13:51.383134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31119
 
1.1%
3099
 
0.9%
073
 
0.7%
19652
 
0.5%
22950
 
0.5%
2850
 
0.5%
6249
 
0.5%
18649
 
0.5%
22849
 
0.5%
19148
 
0.4%
Other values (857)10049
94.0%
ValueCountFrequency (%)
073
0.7%
11
 
< 0.1%
23
 
< 0.1%
31
 
< 0.1%
42
 
< 0.1%
53
 
< 0.1%
65
 
< 0.1%
76
 
0.1%
810
 
0.1%
92
 
< 0.1%
ValueCountFrequency (%)
11002
< 0.1%
10961
< 0.1%
10921
< 0.1%
10841
< 0.1%
10801
< 0.1%
10752
< 0.1%
10691
< 0.1%
10681
< 0.1%
10671
< 0.1%
10661
< 0.1%

Nombre de trains annulés
Real number (ℝ)

Zeros 

Distinct192
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2853935
Minimum0
Maximum297
Zeros3591
Zeros (%)33.6%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2025-12-01T22:13:51.409587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38
95-th percentile41
Maximum297
Range297
Interquartile range (IQR)8

Descriptive statistics

Standard deviation23.652677
Coefficient of variation (CV)2.5472994
Kurtosis38.24932
Mean9.2853935
Median Absolute Deviation (MAD)2
Skewness5.4649963
Sum99233
Variance559.44914
MonotonicityNot monotonic
2025-12-01T22:13:51.438166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03591
33.6%
11461
13.7%
2887
 
8.3%
3652
 
6.1%
4519
 
4.9%
5352
 
3.3%
7274
 
2.6%
6268
 
2.5%
8214
 
2.0%
9213
 
2.0%
Other values (182)2256
21.1%
ValueCountFrequency (%)
03591
33.6%
11461
13.7%
2887
 
8.3%
3652
 
6.1%
4519
 
4.9%
5352
 
3.3%
6268
 
2.5%
7274
 
2.6%
8214
 
2.0%
9213
 
2.0%
ValueCountFrequency (%)
2971
< 0.1%
2881
< 0.1%
2791
< 0.1%
2771
< 0.1%
2741
< 0.1%
2681
< 0.1%
2661
< 0.1%
2581
< 0.1%
2561
< 0.1%
2521
< 0.1%

Commentaire annulations
Unsupported

Missing  Rejected  Unsupported 

Missing10687
Missing (%)100.0%
Memory size83.6 KiB

Nombre de trains en retard au départ
Real number (ℝ)

High correlation  Zeros 

Distinct452
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.747263
Minimum0
Maximum596
Zeros125
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2025-12-01T22:13:51.466538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q121
median53
Q3127
95-th percentile274
Maximum596
Range596
Interquartile range (IQR)106

Descriptive statistics

Standard deviation89.030121
Coefficient of variation (CV)1.0263162
Kurtosis2.7803255
Mean86.747263
Median Absolute Deviation (MAD)39
Skewness1.6246643
Sum927068
Variance7926.3624
MonotonicityNot monotonic
2025-12-01T22:13:51.495018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9148
 
1.4%
13146
 
1.4%
8140
 
1.3%
20140
 
1.3%
14135
 
1.3%
16134
 
1.3%
10133
 
1.2%
17130
 
1.2%
15129
 
1.2%
6127
 
1.2%
Other values (442)9325
87.3%
ValueCountFrequency (%)
0125
1.2%
166
0.6%
282
0.8%
3107
1.0%
497
0.9%
5124
1.2%
6127
1.2%
7122
1.1%
8140
1.3%
9148
1.4%
ValueCountFrequency (%)
5961
< 0.1%
5912
< 0.1%
5861
< 0.1%
5661
< 0.1%
5641
< 0.1%
5622
< 0.1%
5581
< 0.1%
5491
< 0.1%
5481
< 0.1%
5321
< 0.1%
Distinct10238
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.951464
Minimum0
Maximum316.1881
Zeros123
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2025-12-01T22:13:51.522948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.9941389
Q15.9031458
median10.009368
Q315.235088
95-th percentile26.360452
Maximum316.1881
Range316.1881
Interquartile range (IQR)9.3319419

Descriptive statistics

Standard deviation11.726851
Coefficient of variation (CV)0.98120624
Kurtosis156.00142
Mean11.951464
Median Absolute Deviation (MAD)4.5627336
Skewness9.0421413
Sum127725.29
Variance137.51903
MonotonicityNot monotonic
2025-12-01T22:13:51.550133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0123
 
1.2%
212
 
0.1%
18
 
0.1%
37
 
0.1%
57
 
0.1%
96
 
0.1%
116
 
0.1%
66
 
0.1%
105
 
< 0.1%
8.8333333334
 
< 0.1%
Other values (10228)10503
98.3%
ValueCountFrequency (%)
0123
1.2%
0.11
 
< 0.1%
0.1166666672
 
< 0.1%
0.1466666671
 
< 0.1%
0.1833333331
 
< 0.1%
0.2166666671
 
< 0.1%
0.3680555561
 
< 0.1%
0.3954545451
 
< 0.1%
0.4283333331
 
< 0.1%
0.4326330531
 
< 0.1%
ValueCountFrequency (%)
316.18809521
< 0.1%
270.43809521
< 0.1%
246.38333331
< 0.1%
245.08131311
< 0.1%
239.67839511
< 0.1%
229.72611111
< 0.1%
211.6629631
< 0.1%
196.21055561
< 0.1%
173.72260271
< 0.1%
166.16851851
< 0.1%

Retard moyen de tous les trains au départ
Real number (ℝ)

High correlation  Zeros 

Distinct10503
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0466455
Minimum-229.26944
Maximum84.516667
Zeros111
Zeros (%)1.0%
Negative162
Negative (%)1.5%
Memory size83.6 KiB
2025-12-01T22:13:51.577269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-229.26944
5-th percentile0.22121117
Q11.1727922
median2.2662857
Q33.8761042
95-th percentile7.9509371
Maximum84.516667
Range313.78611
Interquartile range (IQR)2.703312

Descriptive statistics

Standard deviation4.9040853
Coefficient of variation (CV)1.6096672
Kurtosis566.52451
Mean3.0466455
Median Absolute Deviation (MAD)1.257102
Skewness-8.1942807
Sum32559.501
Variance24.050053
MonotonicityNot monotonic
2025-12-01T22:13:51.605244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0111
 
1.0%
0.2857142864
 
< 0.1%
0.454
 
< 0.1%
1.3333333334
 
< 0.1%
1.3666666673
 
< 0.1%
-0.1789473683
 
< 0.1%
1.6638888893
 
< 0.1%
1.2885735082
 
< 0.1%
2.9950574712
 
< 0.1%
0.9333333332
 
< 0.1%
Other values (10493)10549
98.7%
ValueCountFrequency (%)
-229.26944441
< 0.1%
-112.26201551
< 0.1%
-92.768307091
< 0.1%
-69.838288291
< 0.1%
-67.953740161
< 0.1%
-12.982278481
< 0.1%
-7.6869791671
< 0.1%
-5.7128205131
< 0.1%
-4.6186666671
< 0.1%
-3.4908888891
< 0.1%
ValueCountFrequency (%)
84.516666671
< 0.1%
83.523611111
< 0.1%
70.343650791
< 0.1%
70.20049021
< 0.1%
69.588787881
< 0.1%
66.491327911
< 0.1%
65.126775961
< 0.1%
64.114225591
< 0.1%
57.359456261
< 0.1%
55.547021281
< 0.1%

Commentaire retards au départ
Unsupported

Missing  Rejected  Unsupported 

Missing10687
Missing (%)100.0%
Memory size83.6 KiB

Nombre de trains en retard à l'arrivée
Real number (ℝ)

High correlation  Zeros 

Distinct196
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.628988
Minimum0
Maximum376
Zeros188
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2025-12-01T22:13:51.631420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q115
median29
Q350
95-th percentile97.7
Maximum376
Range376
Interquartile range (IQR)35

Descriptive statistics

Standard deviation30.678908
Coefficient of variation (CV)0.83755815
Kurtosis5.2949216
Mean36.628988
Median Absolute Deviation (MAD)16
Skewness1.7444128
Sum391454
Variance941.19539
MonotonicityNot monotonic
2025-12-01T22:13:51.660456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17212
 
2.0%
21212
 
2.0%
14207
 
1.9%
15206
 
1.9%
11204
 
1.9%
13202
 
1.9%
19202
 
1.9%
16200
 
1.9%
6195
 
1.8%
26195
 
1.8%
Other values (186)8652
81.0%
ValueCountFrequency (%)
0188
1.8%
1110
1.0%
2140
1.3%
3159
1.5%
4143
1.3%
5186
1.7%
6195
1.8%
7171
1.6%
8170
1.6%
9185
1.7%
ValueCountFrequency (%)
3761
< 0.1%
2551
< 0.1%
2541
< 0.1%
2391
< 0.1%
2352
< 0.1%
2291
< 0.1%
2201
< 0.1%
2172
< 0.1%
2102
< 0.1%
2061
< 0.1%

Retard moyen des trains en retard à l'arrivée
Real number (ℝ)

High correlation  Zeros 

Distinct10226
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.665571
Minimum-40.109259
Maximum299.6
Zeros222
Zeros (%)2.1%
Negative2
Negative (%)< 0.1%
Memory size83.6 KiB
2025-12-01T22:13:51.688223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-40.109259
5-th percentile14.876222
Q125.328252
median32.987198
Q341.91072
95-th percentile60.931974
Maximum299.6
Range339.70926
Interquartile range (IQR)16.582468

Descriptive statistics

Standard deviation15.642794
Coefficient of variation (CV)0.45124869
Kurtosis18.421778
Mean34.665571
Median Absolute Deviation (MAD)8.233652
Skewness2.0205571
Sum370470.96
Variance244.69699
MonotonicityNot monotonic
2025-12-01T22:13:51.716767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0222
 
2.1%
256
 
0.1%
40.55
 
< 0.1%
404
 
< 0.1%
424
 
< 0.1%
444
 
< 0.1%
324
 
< 0.1%
344
 
< 0.1%
41.34
 
< 0.1%
22.54
 
< 0.1%
Other values (10216)10426
97.6%
ValueCountFrequency (%)
-40.109259261
 
< 0.1%
-30.51251
 
< 0.1%
0222
2.1%
5.2666666671
 
< 0.1%
5.6722222221
 
< 0.1%
6.7666666671
 
< 0.1%
7.0166666671
 
< 0.1%
7.3333333331
 
< 0.1%
7.8516666671
 
< 0.1%
7.990476191
 
< 0.1%
ValueCountFrequency (%)
299.61
< 0.1%
255.86666671
< 0.1%
218.69166671
< 0.1%
1901
< 0.1%
187.011
< 0.1%
164.39166671
< 0.1%
1601
< 0.1%
157.13333331
< 0.1%
147.71666671
< 0.1%
141.28333331
< 0.1%

Retard moyen de tous les trains à l'arrivée
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct10494
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8535103
Minimum-472.63889
Maximum92
Zeros121
Zeros (%)1.1%
Negative116
Negative (%)1.1%
Memory size83.6 KiB
2025-12-01T22:13:51.767511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-472.63889
5-th percentile1.1680003
Q13.3057146
median5.1926606
Q37.8595631
95-th percentile13.170421
Maximum92
Range564.63889
Interquartile range (IQR)4.5538485

Descriptive statistics

Standard deviation7.1825141
Coefficient of variation (CV)1.2270439
Kurtosis1960.1481
Mean5.8535103
Median Absolute Deviation (MAD)2.1697181
Skewness-32.311198
Sum62556.465
Variance51.588509
MonotonicityNot monotonic
2025-12-01T22:13:51.794526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0121
 
1.1%
8.23
 
< 0.1%
7.1164583333
 
< 0.1%
2.9083333333
 
< 0.1%
7.0395061732
 
< 0.1%
4.1354166672
 
< 0.1%
12.729614872
 
< 0.1%
5.499486052
 
< 0.1%
9.9794549272
 
< 0.1%
2.2266666672
 
< 0.1%
Other values (10484)10545
98.7%
ValueCountFrequency (%)
-472.63888891
< 0.1%
-173.07696971
< 0.1%
-163.04198311
< 0.1%
-151.29100781
< 0.1%
-150.56211421
< 0.1%
-90.63083991
< 0.1%
-80.855112881
< 0.1%
-71.528828831
< 0.1%
-68.928083991
< 0.1%
-15.192547431
< 0.1%
ValueCountFrequency (%)
921
< 0.1%
83.388888891
< 0.1%
61.816666671
< 0.1%
37.838235291
< 0.1%
36.816869921
< 0.1%
33.97976191
< 0.1%
32.978571431
< 0.1%
32.409090911
< 0.1%
31.214285711
< 0.1%
30.810256411
< 0.1%
Distinct302
Distinct (%)43.3%
Missing9989
Missing (%)93.5%
Memory size83.6 KiB
2025-12-01T22:13:51.877164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2678
Median length1293.5
Mean length547.84527
Min length6

Characters and Unicode

Total characters382396
Distinct characters102
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique188 ?
Unique (%)26.9%

Sample

1st rowLe 9760 heurte un chevreuil vers Le-Creusot-Montchanin-TGV
2nd rowCe mois-ci, l'OD a été touchée par les incidents suivants : Le 1er : Tempête Carmen sur la façade Atlantique (52 TGV ; 1079mn) Le 3 : Tempête Eleonor sur l’ouest de la France (35 TGV ; 407mn) Le 5 : Dérangement d’une aiguille en gare de Massy TGV (57 TGV ; 1254mn) Le 7 : Dérangement du poste d’aiguillage de Paris Montparnasse (70 TGV ; 1490mn) Le 12 : Colis suspect en gare de Paris Montparnasse (32 TGV ; 499mn) Le 17 : Dérangement du poste d’aiguillage de Paris Montparnasse (26 TGV ; 386mn)
3rd rowHeurt d’un chevreuil par OUIGO 7801 vers Valence-TGV
4th rowDérangement de commutateur sur LN1 vers Tonnerre
5th rowCe mois-ci, l'OD a été touchée par les incidents suivants : Le 1er : Tempête Carmen sur la façade Atlantique (52 TGV ; 1079mn) Le 3 : Tempête Eleonor sur l’ouest de la France (35 TGV ; 407mn) Le 5 : Dérangement d’une aiguille en gare de Massy TGV (57 TGV ; 1254mn) Le 7 : Dérangement du poste d’aiguillage de Paris Montparnasse (70 TGV ; 1490mn) Le 12 : Colis suspect en gare de Paris Montparnasse (32 TGV ; 499mn) Le 12 : Incident caténaire en gare de Rennes (26 TGV ; 2188mn) Le 17 : Dérangement du poste d’aiguillage de Paris Montparnasse (26 TGV ; 386mn) Le 17 : Présence d’une personne dans les voies à Ste Luce (11 TGV ; 212mn) Le 26 : Heurt d’une personne au niveau de Tiercé (44 TGV ; 1578mn)
ValueCountFrequency (%)
5131
 
7.6%
tgv2533
 
3.8%
le2500
 
3.7%
de2488
 
3.7%
à2028
 
3.0%
pour1836
 
2.7%
la1617
 
2.4%
minutes1368
 
2.0%
perdues1331
 
2.0%
sur1263
 
1.9%
Other values (2892)45052
67.1%
2025-12-01T22:13:51.991160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
65182
17.0%
e37383
 
9.8%
n21651
 
5.7%
s21067
 
5.5%
r19105
 
5.0%
t18819
 
4.9%
a18661
 
4.9%
i17527
 
4.6%
u15670
 
4.1%
l11187
 
2.9%
Other values (92)136144
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)382396
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
65182
17.0%
e37383
 
9.8%
n21651
 
5.7%
s21067
 
5.5%
r19105
 
5.0%
t18819
 
4.9%
a18661
 
4.9%
i17527
 
4.6%
u15670
 
4.1%
l11187
 
2.9%
Other values (92)136144
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)382396
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
65182
17.0%
e37383
 
9.8%
n21651
 
5.7%
s21067
 
5.5%
r19105
 
5.0%
t18819
 
4.9%
a18661
 
4.9%
i17527
 
4.6%
u15670
 
4.1%
l11187
 
2.9%
Other values (92)136144
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)382396
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
65182
17.0%
e37383
 
9.8%
n21651
 
5.7%
s21067
 
5.5%
r19105
 
5.0%
t18819
 
4.9%
a18661
 
4.9%
i17527
 
4.6%
u15670
 
4.1%
l11187
 
2.9%
Other values (92)136144
35.6%

Nombre trains en retard > 15min
Real number (ℝ)

High correlation  Zeros 

Distinct155
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.074202
Minimum0
Maximum312
Zeros219
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2025-12-01T22:13:52.017865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median21
Q336
95-th percentile68
Maximum312
Range312
Interquartile range (IQR)26

Descriptive statistics

Standard deviation22.027028
Coefficient of variation (CV)0.84478245
Kurtosis7.752928
Mean26.074202
Median Absolute Deviation (MAD)12
Skewness1.9662673
Sum278655
Variance485.18998
MonotonicityNot monotonic
2025-12-01T22:13:52.046160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6311
 
2.9%
10303
 
2.8%
9296
 
2.8%
5293
 
2.7%
8290
 
2.7%
11282
 
2.6%
7282
 
2.6%
13262
 
2.5%
17261
 
2.4%
14261
 
2.4%
Other values (145)7846
73.4%
ValueCountFrequency (%)
0219
2.0%
1139
1.3%
2184
1.7%
3225
2.1%
4219
2.0%
5293
2.7%
6311
2.9%
7282
2.6%
8290
2.7%
9296
2.8%
ValueCountFrequency (%)
3121
< 0.1%
2091
< 0.1%
1991
< 0.1%
1921
< 0.1%
1791
< 0.1%
1721
< 0.1%
1651
< 0.1%
1621
< 0.1%
1571
< 0.1%
1562
< 0.1%
Distinct10129
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.010654
Minimum-4
Maximum299.6
Zeros203
Zeros (%)1.9%
Negative8
Negative (%)0.1%
Memory size83.6 KiB
2025-12-01T22:13:52.074149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile3.163638
Q125.977778
median36.6875
Q345.97376
95-th percentile64.623139
Maximum299.6
Range303.6
Interquartile range (IQR)19.995982

Descriptive statistics

Standard deviation19.709259
Coefficient of variation (CV)0.56295031
Kurtosis6.4685668
Mean35.010654
Median Absolute Deviation (MAD)9.725
Skewness0.69704133
Sum374158.86
Variance388.45487
MonotonicityNot monotonic
2025-12-01T22:13:52.101734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0203
 
1.9%
275
 
< 0.1%
345
 
< 0.1%
455
 
< 0.1%
41.55
 
< 0.1%
384
 
< 0.1%
22.54
 
< 0.1%
34.833333334
 
< 0.1%
59.54
 
< 0.1%
51.54
 
< 0.1%
Other values (10119)10444
97.7%
ValueCountFrequency (%)
-41
 
< 0.1%
-2.7142857141
 
< 0.1%
-2.4042955331
 
< 0.1%
-1.5714285711
 
< 0.1%
-0.6010233921
 
< 0.1%
-0.3518759021
 
< 0.1%
-0.326543211
 
< 0.1%
-0.1607142861
 
< 0.1%
0203
1.9%
0.0513452911
 
< 0.1%
ValueCountFrequency (%)
299.61
< 0.1%
255.86666671
< 0.1%
218.69166671
< 0.1%
187.011
< 0.1%
169.61666671
< 0.1%
164.39166671
< 0.1%
1601
< 0.1%
157.13333331
< 0.1%
147.71666671
< 0.1%
141.28333331
< 0.1%

Nombre trains en retard > 30min
Real number (ℝ)

High correlation  Zeros 

Distinct89
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.485075
Minimum0
Maximum202
Zeros465
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2025-12-01T22:13:52.128720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median9
Q317
95-th percentile35
Maximum202
Range202
Interquartile range (IQR)13

Descriptive statistics

Standard deviation11.589545
Coefficient of variation (CV)0.92827193
Kurtosis12.36723
Mean12.485075
Median Absolute Deviation (MAD)6
Skewness2.3075154
Sum133428
Variance134.31755
MonotonicityNot monotonic
2025-12-01T22:13:52.157791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5615
 
5.8%
3609
 
5.7%
4600
 
5.6%
2592
 
5.5%
7560
 
5.2%
6533
 
5.0%
8521
 
4.9%
0465
 
4.4%
9450
 
4.2%
1443
 
4.1%
Other values (79)5299
49.6%
ValueCountFrequency (%)
0465
4.4%
1443
4.1%
2592
5.5%
3609
5.7%
4600
5.6%
5615
5.8%
6533
5.0%
7560
5.2%
8521
4.9%
9450
4.2%
ValueCountFrequency (%)
2021
 
< 0.1%
1091
 
< 0.1%
1061
 
< 0.1%
941
 
< 0.1%
931
 
< 0.1%
911
 
< 0.1%
891
 
< 0.1%
861
 
< 0.1%
823
< 0.1%
812
< 0.1%

Nombre trains en retard > 60min
Real number (ℝ)

High correlation  Zeros 

Distinct47
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6659493
Minimum0
Maximum71
Zeros1706
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2025-12-01T22:13:52.185551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile15
Maximum71
Range71
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.3226202
Coefficient of variation (CV)1.1407368
Kurtosis10.637669
Mean4.6659493
Median Absolute Deviation (MAD)2
Skewness2.5017704
Sum49865
Variance28.330286
MonotonicityNot monotonic
2025-12-01T22:13:52.213596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
01706
16.0%
11604
15.0%
21401
13.1%
31112
10.4%
4933
8.7%
5752
7.0%
6552
 
5.2%
7479
 
4.5%
8410
 
3.8%
9275
 
2.6%
Other values (37)1463
13.7%
ValueCountFrequency (%)
01706
16.0%
11604
15.0%
21401
13.1%
31112
10.4%
4933
8.7%
5752
7.0%
6552
 
5.2%
7479
 
4.5%
8410
 
3.8%
9275
 
2.6%
ValueCountFrequency (%)
711
 
< 0.1%
591
 
< 0.1%
511
 
< 0.1%
491
 
< 0.1%
461
 
< 0.1%
441
 
< 0.1%
401
 
< 0.1%
391
 
< 0.1%
382
< 0.1%
373
< 0.1%

Prct retard pour causes externes
Real number (ℝ)

Zeros 

Distinct1804
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.724222
Minimum0
Maximum100
Zeros1039
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2025-12-01T22:13:52.242117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.526316
median19.354839
Q330.128734
95-th percentile50
Maximum100
Range100
Interquartile range (IQR)19.602418

Descriptive statistics

Standard deviation16.115659
Coefficient of variation (CV)0.74182904
Kurtosis2.7279209
Mean21.724222
Median Absolute Deviation (MAD)9.6774194
Skewness1.2160994
Sum232166.76
Variance259.71447
MonotonicityNot monotonic
2025-12-01T22:13:52.270173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01039
 
9.7%
25277
 
2.6%
20265
 
2.5%
33.33333333248
 
2.3%
50193
 
1.8%
14.28571429184
 
1.7%
16.66666667141
 
1.3%
40135
 
1.3%
12.5133
 
1.2%
28.57142857132
 
1.2%
Other values (1794)7940
74.3%
ValueCountFrequency (%)
01039
9.7%
0.952380951
 
< 0.1%
1.041666671
 
< 0.1%
1.3333333331
 
< 0.1%
1.369863011
 
< 0.1%
1.428571432
 
< 0.1%
1.56251
 
< 0.1%
1.587301591
 
< 0.1%
1.612903231
 
< 0.1%
1.724137931
 
< 0.1%
ValueCountFrequency (%)
10054
0.5%
93.333333331
 
< 0.1%
90.909090911
 
< 0.1%
90.756302521
 
< 0.1%
88.888888892
 
< 0.1%
88.888888891
 
< 0.1%
851
 
< 0.1%
84.615384621
 
< 0.1%
84.444444441
 
< 0.1%
83.333333333
 
< 0.1%

Prct retard pour cause infrastructure
Real number (ℝ)

Zeros 

Distinct1699
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.951504
Minimum0
Maximum100
Zeros961
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2025-12-01T22:13:52.297983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median20
Q329.708615
95-th percentile48.873385
Maximum100
Range100
Interquartile range (IQR)17.708615

Descriptive statistics

Standard deviation15.110935
Coefficient of variation (CV)0.68837811
Kurtosis3.714082
Mean21.951504
Median Absolute Deviation (MAD)8.7356322
Skewness1.268007
Sum234595.72
Variance228.34035
MonotonicityNot monotonic
2025-12-01T22:13:52.326277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0961
 
9.0%
25351
 
3.3%
20280
 
2.6%
33.33333333262
 
2.5%
16.66666667203
 
1.9%
14.28571429183
 
1.7%
50166
 
1.6%
12.5144
 
1.3%
28.57142857144
 
1.3%
40128
 
1.2%
Other values (1689)7865
73.6%
ValueCountFrequency (%)
0961
9.0%
0.75187971
 
< 0.1%
1.3513513511
 
< 0.1%
1.3698630141
 
< 0.1%
1.4492753621
 
< 0.1%
1.5384615381
 
< 0.1%
1.6129032261
 
< 0.1%
1.6666666672
 
< 0.1%
1.694915251
 
< 0.1%
1.7241379291
 
< 0.1%
ValueCountFrequency (%)
10051
0.5%
97.29729731
 
< 0.1%
94.444444441
 
< 0.1%
93.751
 
< 0.1%
93.333333331
 
< 0.1%
92.857142863
 
< 0.1%
92.307692311
 
< 0.1%
90.909090911
 
< 0.1%
88.888888891
 
< 0.1%
87.51
 
< 0.1%

Prct retard pour cause gestion trafic
Real number (ℝ)

Zeros 

Distinct1759
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.004786
Minimum0
Maximum100
Zeros1278
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2025-12-01T22:13:52.353319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median18.181818
Q328.070175
95-th percentile45.833333
Maximum100
Range100
Interquartile range (IQR)18.070175

Descriptive statistics

Standard deviation14.734253
Coefficient of variation (CV)0.73653641
Kurtosis3.1960236
Mean20.004786
Median Absolute Deviation (MAD)8.951049
Skewness1.1914193
Sum213791.15
Variance217.09822
MonotonicityNot monotonic
2025-12-01T22:13:52.381969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01278
 
12.0%
25294
 
2.8%
20267
 
2.5%
14.28571429207
 
1.9%
33.33333333193
 
1.8%
16.66666667183
 
1.7%
12.5176
 
1.6%
28.57142857145
 
1.4%
11.11111111141
 
1.3%
10134
 
1.3%
Other values (1749)7669
71.8%
ValueCountFrequency (%)
01278
12.0%
1.6949152541
 
< 0.1%
1.7543859651
 
< 0.1%
1.8518518521
 
< 0.1%
1.8867924531
 
< 0.1%
21
 
< 0.1%
2.0833333332
 
< 0.1%
2.1276595741
 
< 0.1%
2.2222222222
 
< 0.1%
2.2727272731
 
< 0.1%
ValueCountFrequency (%)
10036
0.3%
97.368421051
 
< 0.1%
93.877551021
 
< 0.1%
92.682926831
 
< 0.1%
88.888888891
 
< 0.1%
86.363636361
 
< 0.1%
86.206896551
 
< 0.1%
85.714285711
 
< 0.1%
84.615384621
 
< 0.1%
84.210526321
 
< 0.1%
Distinct1625
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.960105
Minimum0
Maximum100
Zeros1172
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2025-12-01T22:13:52.409662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median17.197452
Q325.714286
95-th percentile43.478261
Maximum100
Range100
Interquartile range (IQR)15.714286

Descriptive statistics

Standard deviation13.635002
Coefficient of variation (CV)0.71914167
Kurtosis4.014192
Mean18.960105
Median Absolute Deviation (MAD)7.8025478
Skewness1.289064
Sum202626.65
Variance185.91328
MonotonicityNot monotonic
2025-12-01T22:13:52.437807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01172
 
11.0%
20298
 
2.8%
25275
 
2.6%
33.33333333239
 
2.2%
14.28571429235
 
2.2%
12.5191
 
1.8%
16.66666667182
 
1.7%
50129
 
1.2%
18.18181818125
 
1.2%
28.57142857123
 
1.2%
Other values (1615)7718
72.2%
ValueCountFrequency (%)
01172
11.0%
0.8403361341
 
< 0.1%
1.1235955061
 
< 0.1%
1.1363636361
 
< 0.1%
1.5151515151
 
< 0.1%
1.5873015872
 
< 0.1%
1.6393442621
 
< 0.1%
1.7543859652
 
< 0.1%
1.8518518521
 
< 0.1%
1.9607843141
 
< 0.1%
ValueCountFrequency (%)
10034
0.3%
81.818181821
 
< 0.1%
81.481481481
 
< 0.1%
801
 
< 0.1%
75.757575761
 
< 0.1%
755
 
< 0.1%
72.727272731
 
< 0.1%
71.428571431
 
< 0.1%
703
 
< 0.1%
681
 
< 0.1%
Distinct1358
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2507691
Minimum0
Maximum100
Zeros3168
Zeros (%)29.6%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2025-12-01T22:13:52.465720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.6603774
Q310.861139
95-th percentile21.428571
Maximum100
Range100
Interquartile range (IQR)10.861139

Descriptive statistics

Standard deviation8.1022277
Coefficient of variation (CV)1.1174301
Kurtosis18.711389
Mean7.2507691
Median Absolute Deviation (MAD)5.6603774
Skewness2.7542846
Sum77488.97
Variance65.646093
MonotonicityNot monotonic
2025-12-01T22:13:52.494449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03168
29.6%
10145
 
1.4%
12.5145
 
1.4%
11.11111111139
 
1.3%
14.28571429133
 
1.2%
9.090909091118
 
1.1%
6.25118
 
1.1%
7.692307692112
 
1.0%
5.882352941109
 
1.0%
20105
 
1.0%
Other values (1348)6395
59.8%
ValueCountFrequency (%)
03168
29.6%
0.6944444441
 
< 0.1%
0.847457631
 
< 0.1%
0.9174311931
 
< 0.1%
0.9345794391
 
< 0.1%
0.9433962261
 
< 0.1%
0.9615384621
 
< 0.1%
0.9803921571
 
< 0.1%
11
 
< 0.1%
1.010101012
 
< 0.1%
ValueCountFrequency (%)
1009
 
0.1%
801
 
< 0.1%
751
 
< 0.1%
66.666666674
 
< 0.1%
601
 
< 0.1%
5027
0.3%
46.153846151
 
< 0.1%
44.827586211
 
< 0.1%
44.444444441
 
< 0.1%
43.752
 
< 0.1%
Distinct1425
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.628965
Minimum0
Maximum100
Zeros3614
Zeros (%)33.8%
Negative0
Negative (%)0.0%
Memory size83.6 KiB
2025-12-01T22:13:52.522753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.9180328
Q311.111111
95-th percentile25
Maximum100
Range100
Interquartile range (IQR)11.111111

Descriptive statistics

Standard deviation9.7636705
Coefficient of variation (CV)1.2798159
Kurtosis13.357903
Mean7.628965
Median Absolute Deviation (MAD)4.9180328
Skewness2.6698137
Sum81530.749
Variance95.329261
MonotonicityNot monotonic
2025-12-01T22:13:52.550693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03614
33.8%
10137
 
1.3%
12.5137
 
1.3%
11.11111111125
 
1.2%
14.28571429118
 
1.1%
25103
 
1.0%
20100
 
0.9%
6.2587
 
0.8%
584
 
0.8%
7.14285714383
 
0.8%
Other values (1415)6099
57.1%
ValueCountFrequency (%)
03614
33.8%
0.5376344091
 
< 0.1%
0.6172839511
 
< 0.1%
0.6369426751
 
< 0.1%
0.740740741
 
< 0.1%
0.7936507941
 
< 0.1%
0.81
 
< 0.1%
0.8403361341
 
< 0.1%
0.8771929821
 
< 0.1%
0.909090911
 
< 0.1%
ValueCountFrequency (%)
10012
0.1%
83.333333331
 
< 0.1%
801
 
< 0.1%
752
 
< 0.1%
71.428571431
 
< 0.1%
66.666666676
0.1%
66.666666671
 
< 0.1%
61.90476191
 
< 0.1%
61.111111111
 
< 0.1%
602
 
< 0.1%

Interactions

2025-12-01T22:13:50.047762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:40.791845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:41.291782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:41.903610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.366677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.836465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:43.442192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:43.923128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:44.409492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:44.899734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:45.565204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:46.037842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:46.538053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:47.032151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:47.518536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:48.166519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:48.631329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.096016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.563908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:50.074725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:40.818317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:41.317111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:41.928759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.391910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.861909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:43.469043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:43.949384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:44.436606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:44.926135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:45.591015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:46.064571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:46.564507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:47.058857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:47.543750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:48.192536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:48.656788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.121782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.590518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:50.099590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:40.842943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:41.341354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:41.952762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.415463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.886344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:43.492540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:43.974575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:44.461300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:44.951501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:45.614426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:46.090899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:46.589977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:47.084327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:47.566631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:48.216559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:48.680341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.145595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.614647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:50.125185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:40.867794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:41.365264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:41.976604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.439629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.910867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:43.517170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:43.999101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:44.486749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:44.975905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:45.639389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:46.115847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:46.615910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:47.109276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:47.591225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:48.240087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:48.704323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.169186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.639822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:50.151311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:40.892301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:41.533685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.000364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.463821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.936083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:43.541867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:44.025127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:44.512058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:45.001831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-12-01T22:13:41.807549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.269014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.737799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:43.343520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:43.823081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:44.307392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:44.796298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:45.463350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:45.938434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:46.434321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:46.928685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:47.418013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:48.072011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:48.535776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.000777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.467085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.947841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:50.467243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:41.209928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:41.830957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.292447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.761411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:43.367676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:43.847011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:44.332269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:44.821405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:45.488379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:45.962398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:46.459604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:46.953384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:47.442583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:48.094512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:48.558411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.023374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.490665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.973136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:50.492380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:41.236336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:41.854610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.315949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.785857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:43.392064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:43.871780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:44.357098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:44.847188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:45.512603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:45.987490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:46.484922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:46.978972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:47.467168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:48.118444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:48.581737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.046763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.513862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.997276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:50.518060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:41.262804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:41.878739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.340470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:42.810513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:43.416821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:43.896822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:44.383286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:44.872988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:45.539135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:46.011932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:46.511132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:47.004897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:47.492371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:48.142338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:48.606256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.070724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:49.539243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-01T22:13:50.022026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-01T22:13:52.578103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Durée moyenne du trajetNombre de circulations prévuesNombre de trains annulésNombre de trains en retard au départNombre de trains en retard à l'arrivéeNombre trains en retard > 15minNombre trains en retard > 30minNombre trains en retard > 60minPrct retard pour cause gestion en gare et réutilisation de matérielPrct retard pour cause gestion traficPrct retard pour cause infrastructurePrct retard pour cause matériel roulantPrct retard pour cause prise en compte voyageurs (affluence, gestions PSH, correspondances)Prct retard pour causes externesRetard moyen de tous les trains au départRetard moyen de tous les trains à l'arrivéeRetard moyen des trains en retard au départRetard moyen des trains en retard à l'arrivéeRetard moyen trains en retard > 15 (si liaison concurrencée par vol)Service
Durée moyenne du trajet1.000-0.455-0.183-0.334-0.370-0.049-0.0040.027-0.233-0.116-0.007-0.087-0.1340.177-0.0160.3890.0440.5480.1050.516
Nombre de circulations prévues-0.4551.0000.1620.6640.8100.7130.6600.5420.3140.0840.0500.1820.3200.0480.228-0.0710.221-0.0590.0970.374
Nombre de trains annulés-0.1830.1621.0000.1540.1120.0700.0560.0440.002-0.0770.035-0.025-0.0860.1000.065-0.038-0.062-0.099-0.0990.071
Nombre de trains en retard au départ-0.3340.6640.1541.0000.6850.6150.5540.4700.1870.2130.0980.0450.1340.0820.5990.096-0.172-0.0400.1150.153
Nombre de trains en retard à l'arrivée-0.3700.8100.1120.6851.0000.9020.8240.6810.2950.2380.0620.0880.2660.0250.4620.3400.289-0.0300.0850.252
Nombre trains en retard > 15min-0.0490.7130.0700.6150.9021.0000.9400.7950.2480.1450.0540.0960.2400.1200.5090.5210.3670.2570.1590.173
Nombre trains en retard > 30min-0.0040.6600.0560.5540.8240.9401.0000.8730.2370.0760.0500.1100.2220.1600.5250.5570.4290.4220.3080.143
Nombre trains en retard > 60min0.0270.5420.0440.4700.6810.7950.8731.0000.1840.0420.0680.0850.1820.1720.5260.5740.4280.5660.4510.151
Prct retard pour cause gestion en gare et réutilisation de matériel-0.2330.3140.0020.1870.2950.2480.2370.1841.0000.018-0.1170.1080.200-0.1470.1580.0220.263-0.0290.1460.054
Prct retard pour cause gestion trafic-0.1160.084-0.0770.2130.2380.1450.0760.0420.0181.000-0.132-0.093-0.012-0.2020.1330.137-0.069-0.168-0.0490.200
Prct retard pour cause infrastructure-0.0070.0500.0350.0980.0620.0540.0500.068-0.117-0.1321.000-0.169-0.091-0.0810.0070.068-0.0820.0690.0520.172
Prct retard pour cause matériel roulant-0.0870.182-0.0250.0450.0880.0960.1100.0850.108-0.093-0.1691.000-0.010-0.1690.033-0.0410.2140.0540.0320.135
Prct retard pour cause prise en compte voyageurs (affluence, gestions PSH, correspondances)-0.1340.320-0.0860.1340.2660.2400.2220.1820.200-0.012-0.091-0.0101.000-0.1550.0710.0020.2160.0120.2450.105
Prct retard pour causes externes0.1770.0480.1000.0820.0250.1200.1600.172-0.147-0.202-0.081-0.169-0.1551.0000.0430.127-0.0230.2750.1010.143
Retard moyen de tous les trains au départ-0.0160.2280.0650.5990.4620.5090.5250.5260.1580.1330.0070.0330.0710.0431.0000.5230.3360.2830.2240.037
Retard moyen de tous les trains à l'arrivée0.389-0.071-0.0380.0960.3400.5210.5570.5740.0220.1370.068-0.0410.0020.1270.5231.0000.3390.5540.2840.000
Retard moyen des trains en retard au départ0.0440.221-0.062-0.1720.2890.3670.4290.4280.263-0.069-0.0820.2140.216-0.0230.3360.3391.0000.3480.2220.000
Retard moyen des trains en retard à l'arrivée0.548-0.059-0.099-0.040-0.0300.2570.4220.566-0.029-0.1680.0690.0540.0120.2750.2830.5540.3481.0000.5850.109
Retard moyen trains en retard > 15 (si liaison concurrencée par vol)0.1050.097-0.0990.1150.0850.1590.3080.4510.146-0.0490.0520.0320.2450.1010.2240.2840.2220.5851.0000.057
Service0.5160.3740.0710.1530.2520.1730.1430.1510.0540.2000.1720.1350.1050.1430.0370.0000.0000.1090.0571.000

Missing values

2025-12-01T22:13:50.566845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-01T22:13:50.635595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateServiceGare de départGare d'arrivéeDurée moyenne du trajetNombre de circulations prévuesNombre de trains annulésCommentaire annulationsNombre de trains en retard au départRetard moyen des trains en retard au départRetard moyen de tous les trains au départCommentaire retards au départNombre de trains en retard à l'arrivéeRetard moyen des trains en retard à l'arrivéeRetard moyen de tous les trains à l'arrivéeCommentaire retards à l'arrivéeNombre trains en retard > 15minRetard moyen trains en retard > 15 (si liaison concurrencée par vol)Nombre trains en retard > 30minNombre trains en retard > 60minPrct retard pour causes externesPrct retard pour cause infrastructurePrct retard pour cause gestion traficPrct retard pour cause matériel roulantPrct retard pour cause gestion en gare et réutilisation de matérielPrct retard pour cause prise en compte voyageurs (affluence, gestions PSH, correspondances)
02018-01NationalGRENOBLEPARIS LYON1832450NaN378.0270271.212245NaN2346.3144936.123741Le 9760 heurte un chevreuil vers Le-Creusot-Montchanin-TGV256.12374113617.64705952.9411760.00000023.5294125.8823530.000000
12018-01InternationalPARIS LYONITALIE394940NaN2711.2617282.997695NaN2255.68181811.601064NaN2211.60106415633.33333319.04761923.80952414.2857149.5238100.000000
22018-01NationalMARSEILLE ST CHARLESLYON PART DIEU1065577NaN1336.9781951.706333NaN6028.9200005.195333NaN405.19533319523.07692323.07692319.23076923.0769233.8461547.692308
32018-01NationalPARIS NORDDUNKERQUE1162713NaN4611.2365941.797637NaN2928.6896553.738806NaN183.7388069435.71428628.5714297.14285725.0000003.5714290.000000
42018-01NationalANNECYPARIS LYON2241980NaN128.0708330.489141NaN3837.2460538.552525NaN388.55252514523.80952442.8571439.52381014.2857144.7619054.761905
52018-01NationalTOULOUSE MATABIAUPARIS MONTPARNASSE2571840NaN2311.2217391.193931NaN2640.1166677.510507Ce mois-ci, l'OD a été touchée par les incidents suivants :\nLe 1er : Tempête Carmen sur la façade Atlantique (52 TGV ; 1079mn)\nLe 3 : Tempête Eleonor sur l’ouest de la France (35 TGV ; 407mn)\nLe 5 : Dérangement d’une aiguille en gare de Massy TGV (57 TGV ; 1254mn)\nLe 7 : Dérangement du poste d’aiguillage de Paris Montparnasse (70 TGV ; 1490mn)\nLe 12 : Colis suspect en gare de Paris Montparnasse (32 TGV ; 499mn)\nLe 17 : Dérangement du poste d’aiguillage de Paris Montparnasse (26 TGV ; 386mn)267.51050712359.09090922.7272734.5454559.0909094.5454550.000000
62018-01NationalREIMSPARIS EST462301NaN937.4327963.107569NaN6212.8174733.538937NaN203.5389373017.94871841.02564120.5128215.1282050.00000015.384615
72018-01NationalLYON PART DIEUMARNE LA VALLEE1093163NaN22110.3143297.273376NaN6437.1218757.814643NaN537.814643271018.36734734.69387812.24489820.4081636.1224498.163265
82018-01NationalMONTPELLIERPARIS LYON2063402NaN12210.9918034.073619NaN5844.7557477.465582Heurt d’un chevreuil par OUIGO 7801 vers Valence-TGV587.465582311122.00000038.00000012.00000022.0000002.0000004.000000
92018-01InternationalFRANCFORTPARIS EST2241773NaN434.6197671.339751NaN2625.8615386.438985NaN256.4389856130.76923115.38461523.07692315.3846157.6923087.692308
DateServiceGare de départGare d'arrivéeDurée moyenne du trajetNombre de circulations prévuesNombre de trains annulésCommentaire annulationsNombre de trains en retard au départRetard moyen des trains en retard au départRetard moyen de tous les trains au départCommentaire retards au départNombre de trains en retard à l'arrivéeRetard moyen des trains en retard à l'arrivéeRetard moyen de tous les trains à l'arrivéeCommentaire retards à l'arrivéeNombre trains en retard > 15minRetard moyen trains en retard > 15 (si liaison concurrencée par vol)Nombre trains en retard > 30minNombre trains en retard > 60minPrct retard pour causes externesPrct retard pour cause infrastructurePrct retard pour cause gestion traficPrct retard pour cause matériel roulantPrct retard pour cause gestion en gare et réutilisation de matérielPrct retard pour cause prise en compte voyageurs (affluence, gestions PSH, correspondances)
106772025-03NationalVALENCE ALIXAN TGVPARIS LYON1333820NaN20711.3334946.191056NaN6646.2830818.963962NaN4462.975758281818.18181828.78787931.81818210.6060613.0303037.575758
106782025-03NationalANGERS SAINT LAUDPARIS MONTPARNASSE985510NaN1497.9214772.226255NaN3737.8968471.338143NaN2945.16092016529.72973024.32432410.81081118.9189192.70270313.513514
106792025-03NationalPARIS LYONAIX EN PROVENCE TGV1824910NaN4733.0237592.866395NaN3953.3000004.983944NaN3953.300000181125.64102635.8974367.6923087.69230810.25641012.820513
106802025-03NationalSTRASBOURGPARIS EST1256873NaN27211.3088244.554646NaN10533.0677785.354744NaN7640.566009331515.23809518.09523831.42857110.47619012.38095212.380952
106812025-03NationalLAVALPARIS MONTPARNASSE892420NaN6712.2925373.287672NaN2030.8766671.066185NaN1052.4666676526.31578921.05263215.78947421.05263215.7894740.000000
106822025-03NationalPARIS LYONTOULON2362511NaN2125.9722221.947133NaN1947.4219305.422400NaN1947.42193011321.05263236.84210510.52631615.7894745.26315810.526316
106832025-03NationalLYON PART DIEUMARSEILLE ST CHARLES1055071NaN22313.5541116.078986NaN7737.3173166.595652NaN6342.476190321332.46753224.67532515.58441614.2857147.7922085.194805
106842025-03NationalPARIS ESTSTRASBOURG1196799NaN7115.0544601.534701NaN6444.1796884.573209NaN4557.17148124109.3750009.37500023.43750029.6875009.37500018.750000
106852025-03NationalPARIS LYONLE CREUSOT MONTCEAU MONTCHANIN792190NaN1815.7425931.118341NaN1825.4370372.848478NaN1036.100000515.55555627.77777811.11111122.22222211.11111122.222222
106862025-03NationalVANNESPARIS MONTPARNASSE1682930NaN887.2227272.318203NaN3035.1205562.629067NaN2341.74420313426.66666726.66666710.0000006.66666713.33333316.666667